app.py

import os
import gc
import gradio as gr
import numpy as np
import spaces
import torch
import random
import uuid
import tempfile
from PIL import Image
from typing import Iterable
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

import rerun as rr
from gradio_rerun import Rerun

colors.orange_red = colors.Color(
    name="orange_red",
    c50="#FFF0E5",
    c100="#FFE0CC",
    c200="#FFC299",
    c300="#FFA366",
    c400="#FF8533",
    c500="#FF4500",
    c600="#E63E00",
    c700="#CC3700",
    c800="#B33000",
    c900="#992900",
    c950="#802200",
)

class OrangeRedTheme(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.orange_red,
        neutral_hue: colors.Color | str = colors.slate,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            background_fill_primary="*primary_50",
            background_fill_primary_dark="*primary_900",
            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
            button_primary_text_color="white",
            button_primary_text_color_hover="white",
            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_secondary_text_color="black",
            button_secondary_text_color_hover="white",
            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
            slider_color="*secondary_500",
            slider_color_dark="*secondary_600",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
            color_accent_soft="*primary_100",
            block_label_background_fill="*primary_200",
        )

orange_red_theme = OrangeRedTheme()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
print("torch.__version__ =", torch.__version__)
print("Using device:", device)

from diffusers import FlowMatchEulerDiscreteScheduler
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3

dtype = torch.bfloat16

pipe = QwenImageEditPlusPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit-2511",
    transformer=QwenImageTransformer2DModel.from_pretrained(
        "linoyts/Qwen-Image-Edit-Rapid-AIO",
        subfolder='transformer',
        torch_dtype=dtype,
        device_map='cuda'
    ),
    torch_dtype=dtype
).to(device)

try:
    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
    print("Flash Attention 3 Processor set successfully.")
except Exception as e:
    print(f"Warning: Could not set FA3 processor: {e}")

MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp_rerun')
os.makedirs(TMP_DIR, exist_ok=True)

ADAPTER_SPECS = {
    "Multiple-Angles": {
        "repo": "dx8152/Qwen-Edit-2509-Multiple-angles",
        "weights": "镜头转换.safetensors",
        "adapter_name": "multiple-angles"
    },
    "Photo-to-Anime": {
        "repo": "autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
        "weights": "Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
        "adapter_name": "photo-to-anime"
    },
    "Any-Pose": {
        "repo": "lilylilith/AnyPose",
        "weights": "2511-AnyPose-helper-00006000.safetensors",
        "adapter_name": "any-pose"
    },
    "Light-Migration": {
        "repo": "dx8152/Qwen-Edit-2509-Light-Migration",
        "weights": "参考色调.safetensors",
        "adapter_name": "light-migration"
    },
}

LOADED_ADAPTERS = set()

def update_dimensions_on_upload(image):
    if image is None:
        return 1024, 1024
    
    original_width, original_height = image.size
    
    if original_width > original_height:
        new_width = 1024
        aspect_ratio = original_height / original_width
        new_height = int(new_width * aspect_ratio)
    else:
        new_height = 1024
        aspect_ratio = original_width / original_height
        new_width = int(new_height * aspect_ratio)
        
    new_width = (new_width // 8) * 8
    new_height = (new_height // 8) * 8
    
    return new_width, new_height

@spaces.GPU
def infer(
    images,
    prompt,
    lora_adapter,
    seed,
    randomize_seed,
    guidance_scale,
    steps,
    progress=gr.Progress(track_tqdm=True)
):
    gc.collect()
    torch.cuda.empty_cache()

    if not images:
        raise gr.Error("Please upload at least one image to edit.")

    # --- Process Gallery Input ---
    pil_images = []
    if images is not None:
        for item in images:
            try:
                if isinstance(item, tuple) or isinstance(item, list):
                    path_or_img = item[0]
                else:
                    path_or_img = item

                if isinstance(path_or_img, str):
                    pil_images.append(Image.open(path_or_img).convert("RGB"))
                elif isinstance(path_or_img, Image.Image):
                    pil_images.append(path_or_img.convert("RGB"))
                else:
                    pil_images.append(Image.open(path_or_img.name).convert("RGB"))
            except Exception as e:
                print(f"Skipping invalid image item: {e}")
                continue

    if not pil_images:
        raise gr.Error("Could not process uploaded images.")

    # --- Load Adapter ---
    spec = ADAPTER_SPECS.get(lora_adapter)
    if not spec:
        raise gr.Error(f"Configuration not found for: {lora_adapter}")

    adapter_name = spec["adapter_name"]

    if adapter_name not in LOADED_ADAPTERS:
        print(f"--- Downloading and Loading Adapter: {lora_adapter} ---")
        try:
            pipe.load_lora_weights(
                spec["repo"], 
                weight_name=spec["weights"], 
                adapter_name=adapter_name
            )
            LOADED_ADAPTERS.add(adapter_name)
        except Exception as e:
            raise gr.Error(f"Failed to load adapter {lora_adapter}: {e}")
    else:
        print(f"--- Adapter {lora_adapter} is already loaded. ---")

    pipe.set_adapters([adapter_name], adapter_weights=[1.0])

    # --- Setup Generation ---
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator(device=device).manual_seed(seed)
    negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"

    width, height = update_dimensions_on_upload(pil_images[0])

    try:
        progress(0.4, desc="Generating Image...")
        
        result_image = pipe(
            image=pil_images,
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=height,
            width=width,
            num_inference_steps=steps,
            generator=generator,
            true_cfg_scale=guidance_scale,
        ).images[0]
        
        # --- Save Image for Download ---
        run_id = str(uuid.uuid4())
        output_image_path = os.path.join(TMP_DIR, f"{run_id}_output.png")
        result_image.save(output_image_path)

        # --- Rerun Visualization Logic ---
        progress(0.9, desc="Preparing Rerun Visualization...")
        
        # Handle different Rerun SDK versions
        rec = None
        if hasattr(rr, "new_recording"):
            rec = rr.new_recording(application_id="Qwen-Image-Edit", recording_id=run_id)
        elif hasattr(rr, "RecordingStream"):
            rec = rr.RecordingStream(application_id="Qwen-Image-Edit", recording_id=run_id)
        else:
            rr.init("Qwen-Image-Edit", recording_id=run_id, spawn=False)
            rec = rr
            
        # Log inputs
        for i, img in enumerate(pil_images):
            rec.log(f"images/input_{i}", rr.Image(np.array(img)))
            
        # Log result
        rec.log("images/edited_result", rr.Image(np.array(result_image)))
        
        # Save RRD
        rrd_path = os.path.join(TMP_DIR, f"{run_id}.rrd")
        rec.save(rrd_path)
        
        return rrd_path, seed, gr.update(value=output_image_path, visible=True)

    except Exception as e:
        raise e
    finally:
        gc.collect()
        torch.cuda.empty_cache()

@spaces.GPU
def infer_example(images, prompt, lora_adapter):
    if not images:
        return None, 0, gr.update(visible=False)
    
    if isinstance(images, str):
        images = [images]
        
    result_rrd, seed, img_path = infer(
        images=images,
        prompt=prompt,
        lora_adapter=lora_adapter,
        seed=0,
        randomize_seed=True,
        guidance_scale=1.0,
        steps=4
    )
    return result_rrd, seed, img_path

css="""
#col-container {
    margin: 0 auto;
    max-width: 1000px;
}
#main-title h1 {font-size: 2.3em !important;}
"""

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# **Qwen-Image-Edit-2511-LoRAs-Fast**", elem_id="main-title")
        gr.Markdown("Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2511) adapters. Upload one or more images.")

        with gr.Row(equal_height=True):
            with gr.Column():
                images = gr.Gallery(
                    label="Upload Images", 
                    type="filepath", 
                    columns=2, 
                    rows=1, 
                    height=300,
                    allow_preview=True
                )
                
                prompt = gr.Text(
                    label="Edit Prompt",
                    show_label=True,
                    placeholder="e.g., transform into anime..",
                )

                run_button = gr.Button("Edit Image", variant="primary")

            with gr.Column():
                rerun_output = Rerun(
                    label="Rerun Visualization", 
                    height=354
                )
                
                with gr.Row():
                    lora_adapter = gr.Dropdown(
                        label="Choose Editing Style",
                        choices=list(ADAPTER_SPECS.keys()),
                        value="Photo-to-Anime"
                    )
                
                with gr.Accordion("Advanced Settings", open=False, visible=False):
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)


        with gr.Accordion("Run Edit Image and Download Result 📂", open=False, visible=True):
            download_button = gr.DownloadButton(
                label="Download Image", 
                visible=False,
                )
        
        gr.Examples(
            examples=[
                [["examples/B.jpg"], "Transform into anime.", "Photo-to-Anime"],
                [["examples/A.jpeg"], "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
                [["examples/L1.jpg", "examples/L2.jpg"], "Refer to the color tone, remove the original lighting from Image 1, and relight Image 1 based on the lighting and color tone of Image 2.", "Light-Migration"],
                [["examples/P1.jpg", "examples/P2.jpg"], "Make the person in image 1 do the exact same pose of the person in image 2. Changing the style and background of the image of the person in image 1 is undesirable, so don't do it. The new pose should be pixel accurate to the pose we are trying to copy. The position of the arms and head and legs should be the same as the pose we are trying to copy. Change the field of view and angle to match exactly image 2. Head tilt and eye gaze pose should match the person in image 2.", "Any-Pose"],
            ],
            inputs=[images, prompt, lora_adapter],
            outputs=[rerun_output, seed, download_button],
            fn=infer_example,
            cache_examples=False,
            label="Examples"
        )
        
        gr.Markdown("[*](https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast)This is still an experimental Space for Qwen-Image-Edit-2511.")

    run_button.click(
        fn=infer,
        inputs=[images, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
        outputs=[rerun_output, seed, download_button]
    )

if __name__ == "__main__":
    demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)